Deciphering the glioblastoma phenotype by computed tomography radiomics. (July 2021)

• Record Type: Journal Article
• Authors: Compter, Inge; Verduin, Maikel; Shi, Zhenwei; Woodruff, Henry C.; Smeenk, Robert J.; Rozema, Tom; Leijenaar, Ralph T.H.; Monshouwer, René; Eekers, Daniëlle B.P.; Hoeben, Ann; Postma, Alida A.; Dekker, Andre; De Ruysscher, Dirk; Lambin, Philippe; Wee, Leonard
• Is Part Of: Radiotherapy and oncology. Volume 160(2021)
• Page Start: 132
• Page End: 139
• Publication Date: 2021-07
• Subjects: Glioblastoma -- Radiomics -- Computed tomography -- Radiotherapy -- Model development -- Model validation; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.05.002
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: A CT-derived radiomics model can predict OS in patients with a glioblastoma. Discrimination based on the combined clinical and radiomics model was comparable to previous MRI-based models. Qualitatively high-level datasets will support further model development. Abstract: Introduction: Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. Materials and methods: Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/− temozolomide between 2004 and 2015 treated at three independent institutes ( n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan–Meier curves were generated. Results: Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59–0.71. Conclusion: In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.